The present disclosure relates to a method for an energy-aware selective compression scheme for solar-powered wireless sensor networks, a recording medium for performing the same and device for the same, and more particularly, to an energy-aware selective compression scheme in which end-to-end latency is reduced by using extra energy.
A sensor network is being used in several specific applications such as military, healthcare, disaster monitoring, ecosystem monitoring, a smart home, and the like. Such a sensor network includes lightweight nodes having constrained hardware resources.
Specifically, many studies for overcoming short life-cycle of a network due to limited amount of energy of a battery are underway. In some studies among such studies, the problem of short life-cycle of the network is fundamentally addressed by using solar panels and rechargeable battery resources instead of limited battery resources. Specifically, because of periodicity, predictability, high energy density, and the like among energy resources of an energy-harvesting node, solar energy-harvesting sensor networks are being widely used.
Meanwhile, large amount of energy consumption of a sensor network is used for data transmission through radio frequency, and thus, data compression is used as one of the areas of study for resource optimization in the sensor network. The amount of energy used through the radio frequency may be reduced by reducing the data size through the data compression.
The compression has a trade-off between data size and time used for the compression (latency). The trade-off depends on characteristics of applications in the sensor network, but when latency is permitted to some extent, a lot of energy may be reduced through the compression.
Since a general sensor node is powered by a battery in terms of the sensor network, the general sensor node has limited hardware resources and thus, the life-cycle of the network is short. Since the short life-cycle of the network may cause a problem in the overall performance of the network, the problem needs to be addressed. Meanwhile, a solar energy-harvesting sensor node is able to periodically collect energy and predict the collection, has sufficient energy for driving the sensor node, and thus compensates for a limitation of the general sensor node.
However, extra energy generated by charging a lot of energy during daytime is not fully taken advantage of, and thus is not used and eventually is likely to be discarded. That is, an energy optimization problem occurs.
In terms of data compression, although the entire energy consumption of the sensor network is reduced due to a reduction of an amount of energy required for transmitting and receiving data in each sensor node by compressing the data, there is a problem in that processing overhead such as increase of a latency for the data compression, energy use, and the like is present.